text: import tensorflow as tf
  import numpy as np
  
  tf.set_random_seed(777)  # for reproducibility
  learning_rate = 0.1
  
  x_data = [[0, 0],
            [0, 1],
            [1, 0],
            [1, 1]]
  y_data = [[0],
            [1],
            [1],
            [0]]
  x_data = np.array(x_data, dtype=np.float32)
  y_data = np.array(y_data, dtype=np.float32)
  
  X = tf.placeholder(tf.float32, [None, 2], name='x-input')
  Y = tf.placeholder(tf.float32, [None, 1], name='y-input')
  
  with tf.name_scope("layer1") as scope:
      W1 = tf.Variable(tf.random_normal([2, 2]), name='weight1')
      b1 = tf.Variable(tf.random_normal([2]), name='bias1')
      layer1 = tf.sigmoid(tf.matmul(X, W1) + b1)
  
      w1_hist = tf.summary.histogram("weights1", W1)
      b1_hist = tf.summary.histogram("biases1", b1)
      layer1_hist = tf.summary.histogram("layer1", layer1)
  
  
  with tf.name_scope("layer2") as scope:
      W2 = tf.Variable(tf.random_normal([2, 1]), name='weight2')
      b2 = tf.Variable(tf.random_normal([1]), name='bias2')
      hypothesis = tf.sigmoid(tf.matmul(layer1, W2) + b2)
  
      w2_hist = tf.summary.histogram("weights2", W2)
      b2_hist = tf.summary.histogram("biases2", b2)
      hypothesis_hist = tf.summary.histogram("hypothesis", hypothesis)
  
  # cost/loss function
  with tf.name_scope("cost") as scope:
      cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
                             tf.log(1 - hypothesis))
      cost_summ = tf.summary.scalar("cost", cost)
  
  with tf.name_scope("train") as scope:
      train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
  
  # Accuracy computation
  # True if hypothesis>0.5 else False
  predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
  accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))
  accuracy_summ = tf.summary.scalar("accuracy", accuracy)
  
  # Launch graph
  with tf.Session() as sess:
      # tensorboard --logdir=./logs/xor_logs_r0_01
      merged_summary = tf.summary.merge_all()
  #    writer = tf.summary.FileWriter("./logs/xor_logs_r0_01")
      writer = tf.summary.FileWriter("./logs/xor_logs")
      writer.add_graph(sess.graph)  # Show the graph
  
      # Initialize TensorFlow variables
      sess.run(tf.global_variables_initializer())
  
      for step in range(10001):
          summary, _ = sess.run([merged_summary, train], feed_dict={X: x_data, Y: y_data})
          writer.add_summary(summary, global_step=step)
  
          if step % 100 == 0:
              print(step, sess.run(cost, feed_dict={
                    X: x_data, Y: y_data}), sess.run([W1, W2]))
  
      # Accuracy report
      h, c, a = sess.run([hypothesis, predicted, accuracy],
                         feed_dict={X: x_data, Y: y_data})
      print("\nHypothesis: ", h, "\nCorrect: ", c, "\nAccuracy: ", a)